In the current global capital markets, discussions about artificial intelligence (AI) have been unceasing. Market participants continuously assess whether the ongoing wave of AI fervor represents a fleeting technological bubble or a transformative industrial shift capable of reshaping the longstanding financial ecosystem. Leading investment institutions are at the forefront, grappling with the profound impacts of this technological revolution. The industry's ongoing debate about AI's potential to replace traditional roles and identify high-quality sectors with long-term growth prospects often overlooks a crucial point: AI has evolved beyond being a mere auxiliary tool. By integrating with industrial evolution patterns and capital operation logic, AI has fundamentally disrupted the major asset allocation frameworks and risk diversification strategies of both professional institutions and individual investors. The full spectrum of benefits and risks associated with this industry-wide transformation will only become fully evident and assessable after the market stabilizes.

The most apparent impact of technological change in capital markets is the surge in listings among AI supply chain companies, rendering traditional valuation models obsolete. On June 12, 2026 (Eastern Time), Elon Musk's SpaceX made its debut on the Nasdaq under the ticker SPCX, capturing global attention. Unlike traditional aerospace firms that rely on financial metrics such as revenue, cash flow, and profits for valuation, SpaceX's market valuation is deeply intertwined with long-term expectations for AI-driven space commercialization and satellite computing networks, setting a new benchmark for tech enterprise valuations in the AI era. Around the world, a wave of tech innovation companies specializing in computing power, large-scale models, and intelligent applications has flooded secondary markets, continuously breaking records for IPO fundraising and post-listing premiums. The sustained influx of global equity capital into AI-related themes has directly disrupted traditional cross-asset rotation patterns, with market capital flows exhibiting distinct structural characteristics.

For decades, global major asset allocation has operated within established theoretical frameworks, with mean-variance models and the Merrill Lynch Investment Clock serving as core guides for institutional asset allocation. Equities, bonds, commodities, and alternative assets have historically formed referenceable hedging relationships. The industry's operational model was clear and rigid: institutions depended on macro researchers and industry analysts to dissect macroeconomic fundamentals and corporate operating logic, while individual investors achieved asset diversification through standardized products like mutual funds and private funds. Information barriers and disparities in investment research capabilities constituted the core competitive advantages for professional institutions. However, the comprehensive intervention of artificial intelligence has shaken the foundations of this long-established investment research system. Leveraging intelligent agent systems, institutions can now simultaneously capture tens of thousands of alternative data points, including corporate financial reports, online public sentiment, high-frequency industrial chain data, satellite production capacity data, and logistics freight data. Over 70% of standardized investment research tasks and routine trading execution processes have achieved full automation. Leading asset management institutions have seen their overall operational costs decline by approximately 40%, while the number of investable assets covered by single institutions has multiplied several times over, marking a qualitative leap in investment research efficiency.

For the asset management industry, AI brings not only efficiency gains but also profound changes in industry dynamics, with markets exhibiting a clear K-shaped trajectory. Two distinct developmental paths have emerged among institutions: leading firms have taken the lead in completing digital reconstruction across their entire business chains, establishing integrated data mid-platforms and AI-native investment research systems. They embed intelligent decision-making throughout core processes like customer service, portfolio construction, and full-process risk control, building deep competitive barriers through continuous technological iteration. These firms can cater to both mass-market standardized wealth management needs and customize personalized asset allocation solutions for high-net-worth clients, steadily expanding their market share. In contrast, other institutions merely treat AI as a superficial auxiliary tool, simply overlaying algorithmic functions onto existing business processes. While this approach saves transition costs in the short term, their strategy update speeds and excess return generation capabilities gradually fall below industry averages. As performance declines, product management fees continue to drop, compressing the survival space for small and medium-sized public and private fund institutions and accelerating industry consolidation.

Traditional discretionary long-only funds, which rely on field research and logical deduction to uncover excess returns, have encountered developmental bottlenecks. AI quantitative strategies can capture market sentiment, retail trading behaviors, and order flow anomalies in real time, executing portfolio adjustments at millisecond speeds and demonstrating exceptional trading advantages. In the first quarter of 2026, the total assets under management (AUM) of China's quantitative private fund industry surpassed RMB 1.8 trillion, with the number of RMB 10 billion-scale quantitative institutions surpassing that of traditional discretionary long-only firms. Against this backdrop, liquidity and pricing power for small-cap stocks have gradually shifted toward algorithmic trading, significantly reducing the effectiveness of traditional retail short-term trading strategies like momentum chasing. However, technological approaches have inherent shortcomings: industry-wide algorithmic convergence has triggered strategy crowding issues. When multiple quantitative firms simultaneously trigger stop-loss orders, market volatility can amplify sharply in the short term, with new systemic risks quietly accumulating in the market.

The investment ecosystem for ordinary individual investors has also undergone a fundamental transformation due to artificial intelligence. Smart advisory tools have significantly lowered asset allocation thresholds, enabling investors to generate equity-bond allocations and global asset diversification plans through algorithms without needing to master complex financial theories. This has put downward pressure on fee structures for traditional financial advisors' standardized allocation services. However, opportunities come with challenges: the gap in computing power and data capabilities between individual investors and leading institutions continues to widen. The efficiency of retail investors manually gathering information and executing trades pales in comparison to millisecond-speed intelligent algorithms. Institutions use natural language processing to capture retail sentiment across the internet and engage in contrarian trading, rendering retail investors' traditional technical analysis and fundamental stock-picking methods increasingly ineffective. This further marginalizes individual investors in secondary market competition, accelerating the market's wealth redistribution process.

From a holistic perspective on major asset rotation, the AI supercycle has disrupted the asset operation patterns dominated by traditional macroeconomic cycles. Previously, Federal Reserve interest rate policies and global inflation levels served as key variables for pricing core assets like stocks and bonds. Today, the annual AI capital expenditures of just the world's four largest tech giants have surpassed USD 725 billion, with computing chips, servers, optical modules, and upstream electronic components continuously absorbing massive capital inflows. Capital has flocked en masse to tech equity assets, driving up industry concentration in the U.S. stock market. High-volatility tech sectors have intensified market oscillations, posing severe challenges to classical diversified investment logic. Many institutions now recognize that traditional cross-industry diversified holding patterns can no longer effectively hedge against extreme volatility driven by AI-themed market trends. They are compelled to rebuild portfolio hedging systems, increasing allocations to alternative assets like physical assets and commodities to smooth return curves, marking a comprehensive revolution in global major asset allocation frameworks.

Market enthusiasm remains high, with conceptual speculation intertwining with genuine industrial value, leading to divergent capital pricing of targets. During the initial phase, market capital indiscriminately poured into AI infrastructure sectors like computing power and chips, with industry valuations anticipating future years of earnings and displaying significant bubble characteristics. By 2026, market investment logic had returned to rationality, with capital beginning to stratify its allocations: some funds remained committed to upstream hardware enterprises with strong performance realization and profit delivery capabilities, while others focused on enterprise-end AI application implementation sectors. Purely conceptual targets lacking real business support saw their valuations continue to normalize. The inherent "winner-takes-all" nature of the AI industry drives continuous concentration of core resources like computing power, data, and large models toward leading enterprises, intensifying the Matthew effect in secondary markets. Compared to previous tech cycles, current market segment investment difficulty has surged, increasing stock-picking and timing pressures for both institutions and individual investors.

Meanwhile, the concealed systemic risks derived from AI require heightened vigilance across the entire industry. Algorithm black boxes make investment decision traceability difficult, and under extreme market conditions, homogeneous AI strategies operating collectively can easily trigger liquidity crises. Smart advisory tools generating batch identical allocation plans drive capital flows in the same direction, exacerbating cross-market and cross-asset class linked volatility. Currently, the iterative pace of global regulatory systems has not fully kept pace with technological development, with algorithmic trading constraints, smart advisory compliance boundaries, and alternative data usage norms still undergoing continuous refinement. The risks lurking behind these technologies have not been fully exposed.

Undeniably, artificial intelligence has opened up entirely new developmental spaces for the asset management industry. Vast troves of unexplored alternative data have created new sources of excess returns; investment thresholds for niche alternative assets and cross-border market segments continue to decline; and algorithm-based filtering of short-term market noise enables better implementation of long-term value investment philosophies. However, technology remains merely a tool that cannot alter the core essence of the asset management industry—achieving long-term, stable wealth appreciation for investors. Future industry competition will not hinge on human versus AI opposition but rather on comprehensive contests of institutions' data governance capabilities, human-machine collaboration levels, and risk control systems.

The technological wave is unstoppable, and systemic reconstruction of the investment industry has become inevitable. In the short term, market valuation bubbles, industry differentiation, and heightened volatility cannot be avoided. In the long run, only market participants who achieve deep human-machine integration while strictly adhering to risk control bottom lines will secure their footing in the new asset pricing paradigm. Whether this AI-led investment industry revolution ultimately leads to universal investment efficiency benefits or further intensifies capital polarization remains unanswered and will only be determined after multiple complete economic cycles.

Source: China Investor Network